Anne M. Vercoustre
| Anne M. Vercoustre|
(Alternative names for this author)
|Co-authors||James A. Thom, Jovan Pehcevski, Vladimir Naumovski|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Anne M. Vercoustre is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Entity ranking in Wikipedia: utilising categories, links and topic difficulty prediction||Information retrieval||English||2010||Abstract Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.||0||0|
|Entity ranking in Wikipedia||English||2008||The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.||0||0|
|Exploiting Locality of Wikipedia Links in Entity Ranking||Advances in Information Retrieval||English||2008||Information retrieval from web and XML document collections is ever more focused on returning entities instead of web pages or XML elements. There are many research fields involving named entities; one such field is known as entity ranking, where one goal is to rank entities in response to a query supported with a short list of entity examples. In this paper, we describe our approach to ranking entities from the Wikipedia XML document collection. Our approach utilises the known categories and the link structure of Wikipedia, and more importantly, exploits link co-occurrences to improve the effectiveness of entity ranking. Using the broad context of a full Wikipedia page as a baseline, we evaluate two different algorithms for identifying narrow contexts around the entity examples: one that uses predefined types of elements such as paragraphs, lists and tables; and another that dynamically identifies the contexts by utilising the underlying XML document structure. Our experiments demonstrate that the locality of Wikipedia links can be exploited to significantly improve the effectiveness of entity ranking.||0||0|